In many low- and middle-income countries, tailoring instruction to students’ learning levels has emerged as a promising strategy to close learning gaps—whether through teacher-led interventions that promote differentiated instruction (e.g., “Teaching at the Right Level,” TaRL) or computer-adaptive learning software (e.g., “Mindspark”). In the aftermath of the COVID-19 shock, interest in such tailored remediation programs has grown even stronger, and leading experts currently recommend targeting instruction by learning level instead of by grade as a “great buy” for education policymakers.

Surprisingly, this enthusiastic embrace of tailored instruction does not rest on evidence to suggest that personalization truly drives the impacts of such programs. This is because—contrary to what their name might suggest—personalization is just one of many components of “targeted remediation” programs. Take TaRL, for example, which often also provides additional learning time for students and a change in pedagogy. In India, this program is also known as Combined Activities for Maximized Learning (CAMaL, not TaRL)—a name that better captures its package of various intervention components. Or, take “Mindspark.” Aside from personalization, the computer-adaptive learning software also offers many other features, including games, practice exercises, and nudges for students to study more. How, then, do we know whether instructing students “at the right level” fuels the impact of these programs?

Read the full article about personalized instruction by Alejandro J. Ganimian and Andreas de Barros at Brookings.